Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations4559
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory676.7 KiB
Average record size in memory152.0 B

Variable types

DateTime1
Categorical1
Numeric16

Alerts

BP is highly overall correlated with CO and 4 other fieldsHigh correlation
CO is highly overall correlated with BP and 3 other fieldsHigh correlation
CO 8hr is highly overall correlated with BP and 4 other fieldsHigh correlation
NO is highly overall correlated with NO2 and 2 other fieldsHigh correlation
NO2 is highly overall correlated with NO and 2 other fieldsHigh correlation
O3 is highly overall correlated with O3 8hr and 1 other fieldsHigh correlation
O3 8hr is highly overall correlated with O3 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with BP and 6 other fieldsHigh correlation
PM2.5 is highly overall correlated with BP and 6 other fieldsHigh correlation
RH is highly overall correlated with O3 and 2 other fieldsHigh correlation
Rain is highly overall correlated with V Wind SpeedHigh correlation
Solar Rad is highly overall correlated with RHHigh correlation
Temperature is highly overall correlated with BP and 1 other fieldsHigh correlation
V Wind Speed is highly overall correlated with PM10 and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-02-07 11:03:51.156742
Analysis finished2025-02-07 11:05:10.760044
Duration1 minute and 19.6 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Date
Date

Distinct296
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size71.2 KiB
Minimum2012-11-01 00:00:00
Maximum2013-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-07T17:05:10.992061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:05:11.311264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Categorical

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size71.2 KiB
24:00:00
 
209
01:00
 
209
21:00
 
207
09:00
 
205
23:00
 
204
Other values (19)
3525 

Length

Max length8
Median length5
Mean length5.1375302
Min length5

Characters and Unicode

Total characters23422
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21:00
2nd row22:00
3rd row23:00
4th row24:00:00
5th row01:00

Common Values

ValueCountFrequency (%)
24:00:00 209
 
4.6%
01:00 209
 
4.6%
21:00 207
 
4.5%
09:00 205
 
4.5%
23:00 204
 
4.5%
22:00 204
 
4.5%
10:00 203
 
4.5%
20:00 203
 
4.5%
02:00 202
 
4.4%
18:00 199
 
4.4%
Other values (14) 2514
55.1%

Length

2025-02-07T17:05:11.728103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
24:00:00 209
 
4.6%
01:00 209
 
4.6%
21:00 207
 
4.5%
09:00 205
 
4.5%
23:00 204
 
4.5%
22:00 204
 
4.5%
10:00 203
 
4.5%
20:00 203
 
4.5%
02:00 202
 
4.4%
18:00 199
 
4.4%
Other values (14) 2514
55.1%

Most occurring characters

ValueCountFrequency (%)
0 11660
49.8%
: 4768
20.4%
1 2426
 
10.4%
2 1613
 
6.9%
3 578
 
2.5%
4 559
 
2.4%
9 402
 
1.7%
8 387
 
1.7%
7 364
 
1.6%
5 336
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11660
49.8%
: 4768
20.4%
1 2426
 
10.4%
2 1613
 
6.9%
3 578
 
2.5%
4 559
 
2.4%
9 402
 
1.7%
8 387
 
1.7%
7 364
 
1.6%
5 336
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11660
49.8%
: 4768
20.4%
1 2426
 
10.4%
2 1613
 
6.9%
3 578
 
2.5%
4 559
 
2.4%
9 402
 
1.7%
8 387
 
1.7%
7 364
 
1.6%
5 336
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11660
49.8%
: 4768
20.4%
1 2426
 
10.4%
2 1613
 
6.9%
3 578
 
2.5%
4 559
 
2.4%
9 402
 
1.7%
8 387
 
1.7%
7 364
 
1.6%
5 336
 
1.4%

NO
Real number (ℝ)

High correlation 

Distinct1342
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9562031
Minimum0.05
Maximum117.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:12.051805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.31
Q11.83
median3.62
Q35.67
95-th percentile20.269
Maximum117.38
Range117.33
Interquartile range (IQR)3.84

Descriptive statistics

Standard deviation9.4996387
Coefficient of variation (CV)1.5949152
Kurtosis36.491027
Mean5.9562031
Median Absolute Deviation (MAD)1.88
Skewness5.2431025
Sum27154.33
Variance90.243135
MonotonicityNot monotonic
2025-02-07T17:05:12.347826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.93 21
 
0.5%
0.06 19
 
0.4%
4.64 17
 
0.4%
0.05 16
 
0.4%
3.23 15
 
0.3%
4.82 15
 
0.3%
4.85 15
 
0.3%
4.98 15
 
0.3%
0.39 15
 
0.3%
4.84 15
 
0.3%
Other values (1332) 4396
96.4%
ValueCountFrequency (%)
0.05 16
0.4%
0.06 19
0.4%
0.07 14
0.3%
0.08 5
 
0.1%
0.09 3
 
0.1%
0.1 6
 
0.1%
0.11 6
 
0.1%
0.12 9
0.2%
0.13 5
 
0.1%
0.14 11
0.2%
ValueCountFrequency (%)
117.38 1
< 0.1%
116.99 1
< 0.1%
106.37 1
< 0.1%
96.88 1
< 0.1%
94.6 1
< 0.1%
91.33 1
< 0.1%
87.11 1
< 0.1%
86.79 1
< 0.1%
84.61 1
< 0.1%
83.85 1
< 0.1%

NO2
Real number (ℝ)

High correlation 

Distinct1612
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.97585
Minimum0.01
Maximum47.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:12.629855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.82
Q13.11
median5.42
Q39.19
95-th percentile18.22
Maximum47.03
Range47.02
Interquartile range (IQR)6.08

Descriptive statistics

Standard deviation5.7520181
Coefficient of variation (CV)0.82456161
Kurtosis5.5544699
Mean6.97585
Median Absolute Deviation (MAD)2.78
Skewness1.9366415
Sum31802.9
Variance33.085712
MonotonicityNot monotonic
2025-02-07T17:05:12.893982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.57 14
 
0.3%
4.14 11
 
0.2%
2.86 11
 
0.2%
5.28 11
 
0.2%
5.3 10
 
0.2%
2.42 10
 
0.2%
2.83 10
 
0.2%
4.25 10
 
0.2%
4.74 9
 
0.2%
2.43 9
 
0.2%
Other values (1602) 4454
97.7%
ValueCountFrequency (%)
0.01 3
0.1%
0.03 1
 
< 0.1%
0.05 3
0.1%
0.06 7
0.2%
0.07 4
0.1%
0.08 6
0.1%
0.09 4
0.1%
0.1 3
0.1%
0.11 1
 
< 0.1%
0.12 2
 
< 0.1%
ValueCountFrequency (%)
47.03 1
< 0.1%
46.49 1
< 0.1%
40.6 1
< 0.1%
40.26 1
< 0.1%
40.23 1
< 0.1%
39.8 1
< 0.1%
39.19 1
< 0.1%
37.81 1
< 0.1%
37.53 1
< 0.1%
37.27 1
< 0.1%

CO
Real number (ℝ)

High correlation 

Distinct666
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4124457
Minimum0.3
Maximum8.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:13.402573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.7
Q11.25
median1.86
Q32.96
95-th percentile6.18
Maximum8.01
Range7.71
Interquartile range (IQR)1.71

Descriptive statistics

Standard deviation1.6452212
Coefficient of variation (CV)0.68197232
Kurtosis0.94403331
Mean2.4124457
Median Absolute Deviation (MAD)0.76
Skewness1.3181863
Sum10998.34
Variance2.7067528
MonotonicityNot monotonic
2025-02-07T17:05:13.748253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.43 32
 
0.7%
1.49 30
 
0.7%
1.4 29
 
0.6%
1.44 29
 
0.6%
1.02 27
 
0.6%
1.2 27
 
0.6%
1.57 27
 
0.6%
1.12 26
 
0.6%
1.46 26
 
0.6%
1.41 25
 
0.5%
Other values (656) 4281
93.9%
ValueCountFrequency (%)
0.3 4
0.1%
0.32 5
0.1%
0.33 3
0.1%
0.34 2
 
< 0.1%
0.35 3
0.1%
0.36 3
0.1%
0.37 4
0.1%
0.38 3
0.1%
0.39 2
 
< 0.1%
0.4 4
0.1%
ValueCountFrequency (%)
8.01 1
< 0.1%
7.99 1
< 0.1%
7.97 1
< 0.1%
7.96 1
< 0.1%
7.89 1
< 0.1%
7.87 1
< 0.1%
7.85 1
< 0.1%
7.84 1
< 0.1%
7.8 1
< 0.1%
7.79 2
< 0.1%

CO 8hr
Real number (ℝ)

High correlation 

Distinct626
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4133428
Minimum0.31
Maximum7.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:14.054276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.31
5-th percentile0.73
Q11.26
median1.85
Q32.98
95-th percentile6.21
Maximum7.66
Range7.35
Interquartile range (IQR)1.72

Descriptive statistics

Standard deviation1.6279345
Coefficient of variation (CV)0.67455584
Kurtosis0.97765865
Mean2.4133428
Median Absolute Deviation (MAD)0.76
Skewness1.3376467
Sum11002.43
Variance2.6501707
MonotonicityNot monotonic
2025-02-07T17:05:14.446701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 30
 
0.7%
1.21 30
 
0.7%
1.45 29
 
0.6%
1.08 28
 
0.6%
1.51 28
 
0.6%
1.44 27
 
0.6%
1.62 27
 
0.6%
1.04 27
 
0.6%
1.52 27
 
0.6%
1.46 26
 
0.6%
Other values (616) 4280
93.9%
ValueCountFrequency (%)
0.31 1
 
< 0.1%
0.36 6
0.1%
0.37 4
0.1%
0.38 3
0.1%
0.39 4
0.1%
0.4 1
 
< 0.1%
0.41 4
0.1%
0.42 3
0.1%
0.43 4
0.1%
0.45 2
 
< 0.1%
ValueCountFrequency (%)
7.66 1
< 0.1%
7.56 1
< 0.1%
7.49 2
< 0.1%
7.47 2
< 0.1%
7.46 2
< 0.1%
7.45 2
< 0.1%
7.44 1
< 0.1%
7.42 1
< 0.1%
7.39 1
< 0.1%
7.38 1
< 0.1%

O3
Real number (ℝ)

High correlation 

Distinct1950
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.115071
Minimum0.06
Maximum64.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:14.770384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile1.45
Q13.645
median5.87
Q311.96
95-th percentile36.972
Maximum64.16
Range64.1
Interquartile range (IQR)8.315

Descriptive statistics

Standard deviation11.125159
Coefficient of variation (CV)1.0998597
Kurtosis5.0542166
Mean10.115071
Median Absolute Deviation (MAD)3.04
Skewness2.2519018
Sum46114.61
Variance123.76916
MonotonicityNot monotonic
2025-02-07T17:05:15.051405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.71 15
 
0.3%
4.5 15
 
0.3%
4.4 12
 
0.3%
3.7 12
 
0.3%
5.07 11
 
0.2%
3.82 10
 
0.2%
3.98 10
 
0.2%
3.54 10
 
0.2%
1.38 10
 
0.2%
4.47 10
 
0.2%
Other values (1940) 4444
97.5%
ValueCountFrequency (%)
0.06 1
< 0.1%
0.25 1
< 0.1%
0.27 1
< 0.1%
0.38 1
< 0.1%
0.51 1
< 0.1%
0.52 2
< 0.1%
0.53 2
< 0.1%
0.54 1
< 0.1%
0.55 2
< 0.1%
0.56 1
< 0.1%
ValueCountFrequency (%)
64.16 1
< 0.1%
63.79 1
< 0.1%
63.37 1
< 0.1%
62.72 1
< 0.1%
62.05 1
< 0.1%
61.12 1
< 0.1%
60.96 1
< 0.1%
60.14 1
< 0.1%
59.85 1
< 0.1%
59.69 1
< 0.1%

O3 8hr
Real number (ℝ)

High correlation 

Distinct1953
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.302648
Minimum0.66
Maximum56.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:15.598446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile1.959
Q14.09
median6.62
Q312.3
95-th percentile34.063
Maximum56.54
Range55.88
Interquartile range (IQR)8.21

Descriptive statistics

Standard deviation9.8671912
Coefficient of variation (CV)0.95773355
Kurtosis3.6389504
Mean10.302648
Median Absolute Deviation (MAD)3.2
Skewness1.9705509
Sum46969.77
Variance97.361463
MonotonicityNot monotonic
2025-02-07T17:05:15.991619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.27 11
 
0.2%
3.95 11
 
0.2%
5.48 10
 
0.2%
4.78 10
 
0.2%
4.19 10
 
0.2%
4.16 10
 
0.2%
3.31 10
 
0.2%
4.05 10
 
0.2%
3.7 10
 
0.2%
4.45 9
 
0.2%
Other values (1943) 4458
97.8%
ValueCountFrequency (%)
0.66 2
< 0.1%
0.67 1
 
< 0.1%
0.68 2
< 0.1%
0.7 1
 
< 0.1%
0.71 1
 
< 0.1%
0.73 2
< 0.1%
0.74 4
0.1%
0.76 4
0.1%
0.77 1
 
< 0.1%
0.8 4
0.1%
ValueCountFrequency (%)
56.54 1
< 0.1%
56.39 1
< 0.1%
54.53 1
< 0.1%
52.93 1
< 0.1%
52.68 1
< 0.1%
52.01 1
< 0.1%
52 1
< 0.1%
51.62 1
< 0.1%
51.41 1
< 0.1%
50.78 1
< 0.1%

PM2.5
Real number (ℝ)

High correlation 

Distinct3313
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.240472
Minimum0.03
Maximum366.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:16.333527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile6.91
Q116.89
median37.19
Q379.085
95-th percentile168.481
Maximum366.23
Range366.2
Interquartile range (IQR)62.195

Descriptive statistics

Standard deviation54.222696
Coefficient of variation (CV)0.96412236
Kurtosis3.5151811
Mean56.240472
Median Absolute Deviation (MAD)25.01
Skewness1.7258827
Sum256400.31
Variance2940.1008
MonotonicityNot monotonic
2025-02-07T17:05:16.617548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 28
 
0.6%
8 18
 
0.4%
6 18
 
0.4%
7 14
 
0.3%
8.03 14
 
0.3%
17 13
 
0.3%
9 12
 
0.3%
13 12
 
0.3%
9.94 11
 
0.2%
8.97 11
 
0.2%
Other values (3303) 4408
96.7%
ValueCountFrequency (%)
0.03 1
 
< 0.1%
0.08 1
 
< 0.1%
0.14 2
< 0.1%
0.17 3
0.1%
0.41 1
 
< 0.1%
0.97 1
 
< 0.1%
0.98 1
 
< 0.1%
1.02 1
 
< 0.1%
1.09 1
 
< 0.1%
1.14 2
< 0.1%
ValueCountFrequency (%)
366.23 1
< 0.1%
363.54 1
< 0.1%
363.32 1
< 0.1%
338.51 1
< 0.1%
336.94 1
< 0.1%
336.39 1
< 0.1%
331.38 1
< 0.1%
331.09 1
< 0.1%
315.37 1
< 0.1%
313.94 1
< 0.1%

PM10
Real number (ℝ)

High correlation 

Distinct3841
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.939982
Minimum0.17
Maximum600.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:16.886568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile18.927
Q137.955
median72.7
Q3139
95-th percentile257.142
Maximum600.38
Range600.21
Interquartile range (IQR)101.045

Descriptive statistics

Standard deviation81.80088
Coefficient of variation (CV)0.82677274
Kurtosis3.5186723
Mean98.939982
Median Absolute Deviation (MAD)41.13
Skewness1.6528059
Sum451067.38
Variance6691.384
MonotonicityNot monotonic
2025-02-07T17:05:17.251257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 10
 
0.2%
32 9
 
0.2%
33 8
 
0.2%
42 7
 
0.2%
56 6
 
0.1%
34.03 6
 
0.1%
59 6
 
0.1%
44 6
 
0.1%
23 6
 
0.1%
24.06 6
 
0.1%
Other values (3831) 4489
98.5%
ValueCountFrequency (%)
0.17 1
< 0.1%
0.23 1
< 0.1%
1.72 1
< 0.1%
2.46 1
< 0.1%
3.5 1
< 0.1%
4.45 1
< 0.1%
5.06 1
< 0.1%
6.04 1
< 0.1%
6.12 1
< 0.1%
6.69 1
< 0.1%
ValueCountFrequency (%)
600.38 1
< 0.1%
577.54 1
< 0.1%
535.03 1
< 0.1%
533.67 1
< 0.1%
528.32 1
< 0.1%
518.25 1
< 0.1%
504.01 1
< 0.1%
495.45 1
< 0.1%
493.02 1
< 0.1%
484.35 1
< 0.1%

Wind Speed
Real number (ℝ)

Distinct649
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0837179
Minimum0.37
Maximum12.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:17.649039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.2
Q12.08
median2.9
Q33.84
95-th percentile5.55
Maximum12.99
Range12.62
Interquartile range (IQR)1.76

Descriptive statistics

Standard deviation1.4304124
Coefficient of variation (CV)0.46385969
Kurtosis4.2395912
Mean3.0837179
Median Absolute Deviation (MAD)0.87
Skewness1.3707105
Sum14058.67
Variance2.0460797
MonotonicityNot monotonic
2025-02-07T17:05:17.978354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 24
 
0.5%
2.53 22
 
0.5%
1.61 22
 
0.5%
2.07 21
 
0.5%
2.9 21
 
0.5%
2.33 21
 
0.5%
1.31 21
 
0.5%
2.39 20
 
0.4%
2.91 20
 
0.4%
2.35 20
 
0.4%
Other values (639) 4347
95.3%
ValueCountFrequency (%)
0.37 1
 
< 0.1%
0.46 2
< 0.1%
0.48 1
 
< 0.1%
0.49 1
 
< 0.1%
0.5 1
 
< 0.1%
0.51 4
0.1%
0.52 1
 
< 0.1%
0.56 1
 
< 0.1%
0.58 2
< 0.1%
0.59 1
 
< 0.1%
ValueCountFrequency (%)
12.99 1
< 0.1%
12.62 1
< 0.1%
12.41 1
< 0.1%
11.91 1
< 0.1%
11.28 1
< 0.1%
11.08 1
< 0.1%
11.01 1
< 0.1%
10.98 1
< 0.1%
10.91 1
< 0.1%
10.74 1
< 0.1%

Wind Dir
Real number (ℝ)

Distinct4245
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.92751
Minimum0
Maximum359.89
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:18.302635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.851
Q165.4
median146.69
Q3220.35
95-th percentile319.558
Maximum359.89
Range359.89
Interquartile range (IQR)154.95

Descriptive statistics

Standard deviation94.337445
Coefficient of variation (CV)0.62922037
Kurtosis-0.98566994
Mean149.92751
Median Absolute Deviation (MAD)77.73
Skewness0.26588643
Sum683519.53
Variance8899.5534
MonotonicityNot monotonic
2025-02-07T17:05:18.695183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231.15 4
 
0.1%
203.21 4
 
0.1%
15.94 3
 
0.1%
202.64 3
 
0.1%
74.91 3
 
0.1%
21.72 3
 
0.1%
72.33 3
 
0.1%
81.03 3
 
0.1%
197.77 3
 
0.1%
213.87 3
 
0.1%
Other values (4235) 4527
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
0.32 1
< 0.1%
0.35 1
< 0.1%
0.4 1
< 0.1%
0.49 1
< 0.1%
0.91 1
< 0.1%
1.08 1
< 0.1%
1.22 1
< 0.1%
1.27 2
< 0.1%
1.33 1
< 0.1%
ValueCountFrequency (%)
359.89 1
< 0.1%
359.8 1
< 0.1%
359.79 1
< 0.1%
359.77 1
< 0.1%
359.5 2
< 0.1%
359.46 1
< 0.1%
359.32 1
< 0.1%
359.09 1
< 0.1%
358.99 1
< 0.1%
358.72 1
< 0.1%

Temperature
Real number (ℝ)

High correlation 

Distinct1710
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.91107
Minimum9.52
Maximum38.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:19.247620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.52
5-th percentile16.698
Q124.695
median27.79
Q330.055
95-th percentile33.52
Maximum38.05
Range28.53
Interquartile range (IQR)5.36

Descriptive statistics

Standard deviation4.8349642
Coefficient of variation (CV)0.17966451
Kurtosis0.63762922
Mean26.91107
Median Absolute Deviation (MAD)2.61
Skewness-0.88886366
Sum122687.57
Variance23.376879
MonotonicityNot monotonic
2025-02-07T17:05:19.608155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.82 13
 
0.3%
27.78 12
 
0.3%
28.53 12
 
0.3%
27.94 12
 
0.3%
28.33 12
 
0.3%
28.07 12
 
0.3%
27.5 12
 
0.3%
27.05 11
 
0.2%
28.16 11
 
0.2%
28.12 11
 
0.2%
Other values (1700) 4441
97.4%
ValueCountFrequency (%)
9.52 1
< 0.1%
9.61 1
< 0.1%
9.72 1
< 0.1%
9.74 1
< 0.1%
9.8 1
< 0.1%
9.97 1
< 0.1%
10.19 1
< 0.1%
10.74 1
< 0.1%
10.76 1
< 0.1%
10.84 1
< 0.1%
ValueCountFrequency (%)
38.05 1
< 0.1%
38.03 1
< 0.1%
37.78 1
< 0.1%
37.72 1
< 0.1%
37.63 1
< 0.1%
37.58 1
< 0.1%
37.55 1
< 0.1%
37.3 1
< 0.1%
37.05 2
< 0.1%
36.75 1
< 0.1%

RH
Real number (ℝ)

High correlation 

Distinct2902
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.817276
Minimum22.76
Maximum99.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:19.929208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22.76
5-th percentile47.279
Q167.14
median78.9
Q387.115
95-th percentile93.37
Maximum99.33
Range76.57
Interquartile range (IQR)19.975

Descriptive statistics

Standard deviation14.426508
Coefficient of variation (CV)0.19027996
Kurtosis0.44132165
Mean75.817276
Median Absolute Deviation (MAD)9.23
Skewness-0.90344707
Sum345650.96
Variance208.12414
MonotonicityNot monotonic
2025-02-07T17:05:20.241896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.33 12
 
0.3%
85.85 7
 
0.2%
87.3 7
 
0.2%
87.15 6
 
0.1%
87.04 6
 
0.1%
87.52 6
 
0.1%
81.51 5
 
0.1%
76.65 5
 
0.1%
84.07 5
 
0.1%
89.78 5
 
0.1%
Other values (2892) 4495
98.6%
ValueCountFrequency (%)
22.76 1
< 0.1%
23.15 1
< 0.1%
23.38 1
< 0.1%
23.56 1
< 0.1%
24.34 1
< 0.1%
25.05 1
< 0.1%
25.45 1
< 0.1%
25.64 1
< 0.1%
25.87 1
< 0.1%
26.29 1
< 0.1%
ValueCountFrequency (%)
99.33 12
0.3%
99.32 3
 
0.1%
99.3 1
 
< 0.1%
99.29 2
 
< 0.1%
99.28 1
 
< 0.1%
99.27 1
 
< 0.1%
99.26 1
 
< 0.1%
99.19 1
 
< 0.1%
99.07 1
 
< 0.1%
99.06 1
 
< 0.1%

Solar Rad
Real number (ℝ)

High correlation 

Distinct2513
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.45405
Minimum5.06
Maximum1017.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:20.830615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.06
5-th percentile6.89
Q17.45
median10.49
Q3303.18
95-th percentile719.077
Maximum1017.8
Range1012.74
Interquartile range (IQR)295.73

Descriptive statistics

Standard deviation246.8172
Coefficient of variation (CV)1.4147978
Kurtosis0.6563686
Mean174.45405
Median Absolute Deviation (MAD)3.9
Skewness1.3656399
Sum795336
Variance60918.732
MonotonicityNot monotonic
2025-02-07T17:05:21.177634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.35 34
 
0.7%
7.45 34
 
0.7%
7.5 32
 
0.7%
7.56 32
 
0.7%
7.34 31
 
0.7%
7.48 31
 
0.7%
7.66 30
 
0.7%
7.52 30
 
0.7%
7.55 28
 
0.6%
7.57 28
 
0.6%
Other values (2503) 4249
93.2%
ValueCountFrequency (%)
5.06 1
< 0.1%
5.48 1
< 0.1%
5.64 1
< 0.1%
5.7 1
< 0.1%
5.71 1
< 0.1%
5.74 1
< 0.1%
5.84 1
< 0.1%
5.87 1
< 0.1%
5.91 1
< 0.1%
5.94 1
< 0.1%
ValueCountFrequency (%)
1017.8 1
< 0.1%
1004.64 1
< 0.1%
1001.07 1
< 0.1%
998.57 1
< 0.1%
994.91 1
< 0.1%
993.97 1
< 0.1%
985.07 1
< 0.1%
983.61 1
< 0.1%
975.11 1
< 0.1%
974.9 1
< 0.1%

BP
Real number (ℝ)

High correlation 

Distinct2032
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1087.466
Minimum1072.77
Maximum1101.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:21.459631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1072.77
5-th percentile1077.588
Q11082.21
median1087.51
Q31092.77
95-th percentile1097.32
Maximum1101.18
Range28.41
Interquartile range (IQR)10.56

Descriptive statistics

Standard deviation6.3168966
Coefficient of variation (CV)0.0058088222
Kurtosis-1.0144406
Mean1087.466
Median Absolute Deviation (MAD)5.28
Skewness-0.027296065
Sum4957757.4
Variance39.903182
MonotonicityNot monotonic
2025-02-07T17:05:21.817669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1091.01 7
 
0.2%
1092.72 7
 
0.2%
1085.87 7
 
0.2%
1085.78 7
 
0.2%
1084.12 7
 
0.2%
1091.72 7
 
0.2%
1089.4 7
 
0.2%
1095.28 7
 
0.2%
1091.21 7
 
0.2%
1081.79 7
 
0.2%
Other values (2022) 4489
98.5%
ValueCountFrequency (%)
1072.77 1
< 0.1%
1072.98 1
< 0.1%
1073.04 1
< 0.1%
1073.14 2
< 0.1%
1073.16 1
< 0.1%
1073.49 1
< 0.1%
1073.5 1
< 0.1%
1073.55 1
< 0.1%
1073.66 1
< 0.1%
1073.67 1
< 0.1%
ValueCountFrequency (%)
1101.18 1
 
< 0.1%
1100.94 1
 
< 0.1%
1100.89 1
 
< 0.1%
1100.88 2
< 0.1%
1100.75 1
 
< 0.1%
1100.74 1
 
< 0.1%
1100.71 1
 
< 0.1%
1100.63 1
 
< 0.1%
1100.56 3
0.1%
1100.5 1
 
< 0.1%

Rain
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12366528
Minimum0.01
Maximum10.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:22.243581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.01
median0.02
Q30.04
95-th percentile0.36
Maximum10.35
Range10.34
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.58354533
Coefficient of variation (CV)4.7187484
Kurtosis99.185542
Mean0.12366528
Median Absolute Deviation (MAD)0.01
Skewness9.0549939
Sum563.79
Variance0.34052515
MonotonicityNot monotonic
2025-02-07T17:05:22.507600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 1302
28.6%
0.02 1115
24.5%
0.03 827
18.1%
0.04 596
13.1%
0.05 257
 
5.6%
0.06 82
 
1.8%
0.1 15
 
0.3%
0.11 13
 
0.3%
0.07 11
 
0.2%
0.15 11
 
0.2%
Other values (173) 330
 
7.2%
ValueCountFrequency (%)
0.01 1302
28.6%
0.02 1115
24.5%
0.03 827
18.1%
0.04 596
13.1%
0.05 257
 
5.6%
0.06 82
 
1.8%
0.07 11
 
0.2%
0.08 3
 
0.1%
0.09 3
 
0.1%
0.1 15
 
0.3%
ValueCountFrequency (%)
10.35 1
< 0.1%
8.99 1
< 0.1%
8.16 1
< 0.1%
7.9 1
< 0.1%
7.7 1
< 0.1%
7.32 1
< 0.1%
7.24 1
< 0.1%
6.59 1
< 0.1%
6.36 1
< 0.1%
6.25 1
< 0.1%

V Wind Speed
Real number (ℝ)

High correlation 

Distinct217
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7323492
Minimum0.99
Maximum3.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2025-02-07T17:05:22.807250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile1.13
Q11.31
median1.62
Q32.12
95-th percentile2.58
Maximum3.74
Range2.75
Interquartile range (IQR)0.81

Descriptive statistics

Standard deviation0.48491834
Coefficient of variation (CV)0.27991951
Kurtosis-0.63967947
Mean1.7323492
Median Absolute Deviation (MAD)0.37
Skewness0.54950468
Sum7897.78
Variance0.2351458
MonotonicityNot monotonic
2025-02-07T17:05:23.116644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.23 63
 
1.4%
1.2 58
 
1.3%
1.22 56
 
1.2%
1.25 56
 
1.2%
1.33 56
 
1.2%
1.35 55
 
1.2%
1.21 55
 
1.2%
1.28 55
 
1.2%
1.27 54
 
1.2%
1.3 52
 
1.1%
Other values (207) 3999
87.7%
ValueCountFrequency (%)
0.99 1
 
< 0.1%
1 3
 
0.1%
1.01 4
 
0.1%
1.02 8
 
0.2%
1.03 4
 
0.1%
1.04 7
 
0.2%
1.05 6
 
0.1%
1.06 14
0.3%
1.07 18
0.4%
1.08 24
0.5%
ValueCountFrequency (%)
3.74 1
< 0.1%
3.69 1
< 0.1%
3.5 2
< 0.1%
3.48 1
< 0.1%
3.39 1
< 0.1%
3.31 1
< 0.1%
3.17 1
< 0.1%
3.16 1
< 0.1%
3.14 1
< 0.1%
3.1 1
< 0.1%

Interactions

2025-02-07T17:05:04.119098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:03:54.074332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:03:58.346102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:02.972789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:09.067450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:13.268585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:17.800687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:22.386384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:27.008130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:32.271521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:38.649141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:43.105819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:47.147272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:51.813777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:55.984193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:59.894947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:05:04.311552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:03:54.605372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:03:58.527116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:03.277486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:09.277113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:13.490824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:18.078433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:22.755366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:27.271367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:32.966728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:38.987839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:43.314833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:47.357546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:52.127395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:56.167208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:05:00.427538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:05:04.720608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:03:54.971006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:03:58.703129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:03.539508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:09.488641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:13.673842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:18.282450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:22.932558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:27.535386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:33.565456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-07T17:03:58.143432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:02.729772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:08.851375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:13.042908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:17.500662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:21.922349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:26.758040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:31.414715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:38.365120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:42.833189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:46.755373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:51.486753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:55.705598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:04:59.503646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T17:05:03.912083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-07T17:05:23.443054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BPCOCO 8hrNONO2O3O3 8hrPM10PM2.5RHRainSolar RadTemperatureTimeV Wind SpeedWind DirWind Speed
BP1.0000.5970.6220.3630.300-0.117-0.0930.6280.692-0.016-0.3380.030-0.6790.112-0.3690.074-0.161
CO0.5971.0000.9470.4720.247-0.0980.0810.5840.632-0.095-0.277-0.093-0.4730.117-0.3180.142-0.268
CO 8hr0.6220.9471.0000.4360.151-0.107-0.0220.5390.604-0.039-0.259-0.107-0.5390.103-0.2900.133-0.185
NO0.3630.4720.4361.0000.547-0.308-0.0410.5880.6020.064-0.230-0.216-0.3900.126-0.3340.017-0.304
NO20.3000.2470.1510.5471.000-0.2390.1140.5560.5520.033-0.305-0.348-0.3020.199-0.381-0.004-0.411
O3-0.117-0.098-0.107-0.308-0.2391.0000.7790.044-0.037-0.6370.0060.4740.4090.194-0.0490.0310.352
O3 8hr-0.0930.081-0.022-0.0410.1140.7791.0000.2670.184-0.558-0.1160.1700.3190.176-0.229-0.0020.065
PM100.6280.5840.5390.5880.5560.0440.2671.0000.946-0.260-0.444-0.069-0.3570.117-0.5460.118-0.300
PM2.50.6920.6320.6040.6020.552-0.0370.1840.9461.000-0.118-0.443-0.142-0.4880.119-0.5530.137-0.330
RH-0.016-0.095-0.0390.0640.033-0.637-0.558-0.260-0.1181.0000.133-0.528-0.4900.2160.163-0.059-0.183
Rain-0.338-0.277-0.259-0.230-0.3050.006-0.116-0.444-0.4430.1331.0000.0220.1790.0120.628-0.0340.320
Solar Rad0.030-0.093-0.107-0.216-0.3480.4740.170-0.069-0.142-0.5280.0221.0000.3790.3750.0570.0950.257
Temperature-0.679-0.473-0.539-0.390-0.3020.4090.319-0.357-0.488-0.4900.1790.3791.0000.2100.238-0.0340.190
Time0.1120.1170.1030.1260.1990.1940.1760.1170.1190.2160.0120.3750.2101.0000.1210.1320.152
V Wind Speed-0.369-0.318-0.290-0.334-0.381-0.049-0.229-0.546-0.5530.1630.6280.0570.2380.1211.000-0.0900.426
Wind Dir0.0740.1420.1330.017-0.0040.031-0.0020.1180.137-0.059-0.0340.095-0.0340.132-0.0901.000-0.083
Wind Speed-0.161-0.268-0.185-0.304-0.4110.3520.065-0.300-0.330-0.1830.3200.2570.1900.1520.426-0.0831.000

Missing values

2025-02-07T17:05:09.715340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-07T17:05:10.381261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateTimeNONO2COCO 8hrO3O3 8hrPM2.5PM10Wind SpeedWind DirTemperatureRHSolar RadBPRainV Wind Speed
2011/01/201221:003.009.004.674.323.1513.3446.9447.032.27105.5627.1377.747.231092.920.011.16
2111/01/201222:004.288.894.754.402.0410.6145.0647.931.94156.1926.8778.077.611093.210.011.13
2211/01/201223:007.149.454.804.501.037.7846.9745.972.15182.8026.1378.697.481093.190.011.15
2311/01/201224:00:006.967.624.674.570.865.0845.7044.881.81191.9125.7178.537.461093.010.011.12
2411/02/201201:006.136.314.504.610.703.0334.7034.032.65173.5425.2478.137.351092.910.011.20
2511/02/201202:004.324.534.354.610.942.1024.8734.462.79163.8924.6980.787.361092.800.011.21
2611/02/201203:003.172.824.224.581.411.7020.9319.312.56188.7124.5580.847.271092.680.021.19
4611/02/201223:002.997.924.704.817.118.3546.3544.704.14162.2526.2273.287.051090.940.021.36
4711/02/201224:00:003.004.864.504.857.226.6028.0033.942.94159.5526.2569.987.121091.070.031.22
4811/03/201201:002.893.554.414.876.965.5228.3332.122.77171.6426.0866.887.181090.900.021.20
DateTimeNONO2COCO 8hrO3O3 8hrPM2.5PM10Wind SpeedWind DirTemperatureRHSolar RadBPRainV Wind Speed
874410/31/201309:000.298.221.671.6511.347.3265.63105.753.8533.7424.4170.72381.201098.760.031.73
874610/31/201311:0083.856.641.521.6011.188.3355.0097.005.0668.8227.1472.07635.261098.310.031.47
875110/31/201316:000.064.731.651.6716.5114.6053.8398.712.82171.8028.9767.30234.171094.120.011.23
875210/31/201317:000.065.991.661.6613.5614.9248.1486.522.33203.4328.1872.2871.731094.180.011.16
875310/31/201318:000.259.422.011.717.4013.9854.33109.961.62151.8127.1478.279.411094.280.011.07
875510/31/201320:0024.8015.383.172.004.3412.21119.80241.380.4663.0426.0685.497.941095.530.011.00
875610/31/201321:0037.4613.243.842.274.5010.90149.10284.880.60250.9625.6988.557.891096.220.011.03
875710/31/201322:0054.0212.704.182.584.409.28186.17310.061.96319.3225.3987.127.531096.560.011.19
875810/31/201323:0032.1212.903.282.784.177.44155.12246.162.1826.7024.6387.347.561096.660.011.22
875910/31/201324:00:0014.9912.662.532.894.755.9788.45134.192.9524.1423.9085.297.281096.180.011.27